Measurement Bias Estimation in the Problem of Target Tracking

Abstract

In the problem of target tracking, different types of biases can enter into the measurement collected by sensors due to various reasons. In order to accurately track the target, it is essential to estimate and correct the measurement bias. Considering practical backgrounds, the bias is assumed to be locally stationary Gaussian distributed and an iterative estimation algorithm is proposed. Firstly, a mechanism is established to detect whether the bias switches between different Gaussian distributions. Secondly, the expectation maximization algorithm with the assistance of extended Kalman filtering and smoothing is proposed to iteratively estimate the bias and target state in an offline manner. Simulations show the proposed algorithm can suppress the impact of the measurement bias on target tracking.